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Code to import data
list_data_csvs <- list.files(here("data"),".+csv$",include.dirs= FALSE)
offset_rollingmedian <- 51
read_data <- function(files){
read.csv(files) %>%
rename_all(tolower) %>%
#extract protocol from beginning of file name
mutate(protocol = gsub(".+\\_(.+)\\_org.+","\\1",basename(files))) %>%
#extract measurement type from beginning of file name
mutate(measured = gsub("(^[^_]+).+","\\1",basename(files))) %>%
#extract OrganoidID (starting with "org" or "orgconf (for confocal data))
mutate(orgID = gsub(".+\\_(org\\d+|orgconf\\d+).+","\\1",basename(files))) %>%
#extract filename
mutate(filename=basename(files)) %>%
#extract angle
mutate(angle=gsub(".+angle(\\d+).+","\\1",filename)) %>%
mutate(angle=ifelse(angle == 0, "180°",angle)) %>%
mutate(angle=ifelse(angle == 1,"0°",angle)) %>%
#if angle information is missing in filename make it "both angles"
mutate(angle = ifelse(grepl("angle",filename),angle,"both_angles")) %>%
#extract illumination side
mutate(illu=gsub(".+illu(.+)\\_angle.+","\\1",filename)) %>%
mutate(illu=ifelse(illu == 0, "B",illu)) %>%
mutate(illu=ifelse(illu == 1,"A",illu)) %>%
mutate(illu=ifelse(illu == "fused","both_illuminations",illu)) %>%
#if illumination information is missing in the filename make it "both_illuminations"
mutate(illu = ifelse(grepl("illu",filename),illu,"both_illuminations")) %>%
#extract staining type
#if staining information is missing in the filename make it "draq5"
mutate(staining = ifelse(grepl("mito",filename),"mitotracker","draq5")) %>%
#all values (either quality or image feature measurements) should be named "value"
rename_all(~sub('quality', 'value', .x)) %>%
rename_all(~sub('mean','value',.x)) %>%
#slice numbering in "slice" column
mutate(slice = (1:n())-1)
}
data_full <- map_dfr(file.path(here("data"),list_data_csvs),read_data) %>%
rename(min_Img = min, max_Img = max) %>%
select(protocol,orgID,slice,angle,illu,staining,measured, value,max_Img,min_Img) %>%
mutate(measured = ifelse(measured == "Img-features","mean-intensity",measured)) %>%
group_by(protocol,orgID,illu,angle) %>%
mutate(max_slice = max(slice)) %>%
mutate(slice_norm = slice-max_slice/2) %>%
group_by(protocol,orgID,angle,illu,staining,measured) %>%
#contrast is just max minus minimum intensity at a given slice
mutate(contrast = max_Img - min_Img) %>%
mutate(contrast_norm = contrast / max(contrast)) %>%
select(-c(max_Img,min_Img)) %>%
#normalize all measured values between 0 and 1
mutate(value_norm = value / max(value)) %>%
ungroup() %>%
arrange(protocol,orgID)
stepsizes_and_corresponding_orgIDs <- data.frame(
protocol = c("ClearT2","ClearT2","ClearT2",
"Fructose","Fructose","Fructose",
"ScaleA2","ScaleA2","ScaleA2",
"Fructose","Fructose","Fructose",
"Control","Control","Control"),
orgID = c("org1","org2","org3",
"org2","org4","org5",
"org1","org2","org3",
"orgconf1","orgconf2","orgconf3",
"orgconf1","orgconf2","orgconf3"),
stepsize = c(2.42, 2.42, 2.42,
1.7,1.45,1.55,
2.42,2.42,2.42,
2,2,2,
2,2,2))
data_full <- data_full %>%
left_join(stepsizes_and_corresponding_orgIDs,by=c("protocol","orgID")) %>%
mutate(slice_norm_micro = slice_norm * stepsize)
Plot for Figure 2
#select organoid to plot
selected_organoid <- "ScaleA2_org2"
boundaries <- c(15,70,210,260)
selected_slices <- c(7,60,134,220)
boundaries_micron <- filter(data_full,slice %in% boundaries) %>%
#make name
mutate(name = paste0(protocol,"_",orgID)) %>%
#only the organoid we want to plot
filter(name == selected_organoid) %>%
distinct(slice_norm_micro) %>%
pull(slice_norm_micro)
selected_slices_micron <- filter(data_full,slice %in% selected_slices) %>%
#make name
mutate(name = paste0(protocol,"_",orgID)) %>%
#only the organoid we want to plot
filter(name == selected_organoid) %>%
distinct(slice_norm_micro) %>%
pull(slice_norm_micro)
#dataframe describing edge locations
rects <- data.frame(xmin = c(boundaries_micron[1],boundaries_micron[2],boundaries_micron[3]),
xmax = c(boundaries_micron[2],boundaries_micron[3],boundaries_micron[4]),
ymin = -Inf,
ymax = Inf,
fill = c( wes_palette("Moonrise3")[3], wes_palette("Moonrise3")[1], wes_palette("Moonrise3")[3]))
#filter data for plots
data_to_plot <- data_full %>%
#only draq5 staining
filter(staining == "draq5") %>%
#only multiview reconstructed images
filter(angle == "both_angles") %>%
#make name
mutate(name = paste0(protocol,"_",orgID)) %>%
#only the organoid we want to plot
filter(name == selected_organoid)
intensity_plot <- data_to_plot %>%
filter(measured == "mean-intensity") %>%
ggplot(., aes_string(x="slice_norm_micro",y="value"))+
# geom_rect(data = rects, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = fill), inherit.aes = FALSE, alpha = 0.15) +
geom_line(aes(color=name),size=1.5,color="black") +
geom_vline(xintercept=rects$xmin, size=0.3,alpha = 0.15) +
geom_vline(xintercept=rects$xmax, size=0.3,alpha = 0.15) +
theme_bw() +
labs(y = "pixel\nintensity")+
ggtitle("intensity") +
scale_x_continuous(limits = c(min(data_to_plot$slice_norm_micro),max(data_to_plot$slice_norm_micro)),
breaks = c(-300,-200,-100,0,100,200,300))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
plot.title = element_text(size=10))+
geom_vline(xintercept=selected_slices_micron,size=0.15,color="black")
contrast_plot <- data_to_plot %>%
filter(measured == "mean-intensity") %>%
ggplot(., aes_string(x="slice_norm_micro",y="contrast"))+
# geom_rect(data = rects, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = fill), inherit.aes = FALSE, alpha = 0.15) +
geom_line(aes(color=name),size=1.5,color="black") +
geom_vline(xintercept=rects$xmin, size=0.3,alpha = 0.15) +
geom_vline(xintercept=rects$xmax, size=0.3,alpha = 0.15) +
theme_bw() +
labs(y="contrast")+
ggtitle("Contrast (raw)") +
scale_x_continuous(limits= c(min(data_to_plot$slice_norm_micro),max(data_to_plot$slice_norm_micro)),
breaks = c(-300,-200,-100,0,100,200,300))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
plot.title = element_text(size=10))+
geom_vline(xintercept=selected_slices_micron,size=0.15,color="black")
DCT_Shannon_entropy_plot <- data_to_plot %>%
filter(measured == "DCT-Shannon-entropy") %>%
ggplot(., aes_string(x="slice_norm_micro",y="value"))+
#geom_rect(data = rects, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = fill), inherit.aes = FALSE, alpha = 0.15) +
geom_line(aes(color=name),size=1.5,color="black") +
geom_vline(xintercept=rects$xmin, size=0.3,alpha = 0.15) +
geom_vline(xintercept=rects$xmax, size=0.3,alpha = 0.15) +
theme_bw() +
labs(y="DCT shannon entropy",x="µm (relative to center)") +
ggtitle("DCT shannon entropy") +
scale_x_continuous(limits= c(min(data_to_plot$slice_norm_micro),max(data_to_plot$slice_norm_micro)),
breaks = c(-300,-200,-100,0,100,200,300))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
plot.title = element_text(size=10))+
geom_vline(xintercept=selected_slices_micron,size=0.15,color="black")
FRC_plot_relative <- data_to_plot %>%
filter(measured == "FRC") %>%
ggplot(., aes_string(x="slice_norm_micro",y="value"))+
#geom_rect(data = rects, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = fill), inherit.aes = FALSE, alpha = 0.15) +
geom_line(aes(color=name),size=1.5,color="black") +
geom_vline(xintercept=rects$xmin, size=0.3,alpha = 0.15) +
geom_vline(xintercept=rects$xmax, size=0.3,alpha = 0.15) +
theme_bw() +
ggtitle("FRC-QE score") +
scale_x_continuous(limits= c(min(data_to_plot$slice_norm_micro),max(data_to_plot$slice_norm_micro)),
breaks = c(-300,-200,-100,0,100,200,300))+
scale_y_continuous(limits=c(0,filter(data_to_plot, measured == "FRC") %>% pull(value) %>% max()))+
theme(plot.title = element_text(size=10))+
geom_vline(xintercept=selected_slices_micron,size=0.15,color="black")
#put all plots together
patch <- (intensity_plot / contrast_plot / DCT_Shannon_entropy_plot / FRC_plot_relative )
#plot
plot <- patch & theme(panel.border = element_rect(size=2),
axis.text = element_text(face="bold",size=12),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
)
ggsave(plot=plot, file="Figure_2_lineplots.png", type = "cairo-png", bg = "transparent",dpi = 300,width=12)
## Saving 12 x 5 in image
plot
Figure 3 - lightsheet data
#define line plot function for the following figures
line_plot <- function(data_to_plot,X,Y,xlab,ylab,colorby){
ggplot(data_to_plot, aes_string(x=X,y=Y,group="name"))+
geom_line(aes_string(color=colorby),size=1) +
labs(x=xlab, y=ylab) +
facet_grid(~protocol,scale="free_x")+
theme_bw() +
theme(legend.position = "top")
}
unfused_examples <- data_full %>%
mutate(name = paste0(protocol,"_",orgID)) %>%
filter(!grepl("both_angles",angle)) %>%
distinct(name) %>%
pull(name)
selected_slices_micron <- c(0,200)
data_to_plot <- data_full %>%
#only draq5 staining
filter(staining == "draq5") %>%
mutate(name = paste0(protocol,"_",orgID)) %>%
filter(name %in% unfused_examples) %>%
filter(!grepl("ScaleA2",name)) %>%
#select specific organoids
filter(name %in% c("ClearT2_org2", "Fructose_org4")) %>%
mutate(name = paste0(protocol,"_",orgID)) %>%
mutate(microns_from_start = slice * stepsize) %>%
mutate(angle = ifelse(!grepl("both_angles",angle),angle,"multiview fusion")) %>%
filter(measured == "FRC")
#fixing the order
data_to_plot$angle_ordered <- factor(data_to_plot$angle, levels=c("0°","multiview fusion", "180°"))
FRC_plot <- line_plot(data_to_plot,"slice_norm_micro","value","µm (relative to center)","FRC-QE score","name") +
facet_grid(~angle_ordered,scale="free_x") +
scale_y_continuous(limits=c(0, max(data_to_plot$value)))
#put everything together
patch <- FRC_plot
#plot
plot <- patch &
theme(panel.border = element_rect(size=1.5),
axis.text = element_text(face="bold",size=12),
strip.text = element_text(face="bold",size=14)
) &
scale_color_manual(values = wes_palette("Moonrise3")) &
geom_vline(xintercept=selected_slices_micron,linetype='dashed',size=0.8,color="red")
ggsave(plot=plot, file="Figure_3_lightsheet.png", type = "cairo-png", bg = "transparent",dpi = 300,height=4,width=8)
plot
Line Plots for Figure 4
selected_slices_micron <- c(-200,0)
replicates_and_corresponding_orgIDs <- data.frame(protocol = c("ClearT2","ClearT2","ClearT2",
"Fructose","Fructose","Fructose",
"ScaleA2","ScaleA2","ScaleA2"),
orgID = c("org3","org2","org1",
"org5","org4","org2",
"org2","org3","org1"),
replicates = c("replicate 1", "replicate 2", "replicate 3",
"replicate 1","replicate 2","replicate 3",
"replicate 1","replicate 2","replicate 3"))
data_to_plot <- data_full %>%
left_join(replicates_and_corresponding_orgIDs,by=c("orgID","protocol")) %>%
#only fused angles
filter(angle == "both_angles") %>%
#only both illuminations
filter(illu == "both_illuminations") %>%
#only draq5 staining
filter(staining == "draq5") %>%
mutate(name = paste0(protocol,"_",orgID)) %>%
#filter out confocal data
filter(!grepl("orgconf",orgID))
contrast_plot <- data_to_plot %>%
filter(measured == "mean-intensity") %>%
line_plot(.,"slice_norm_micro","contrast","slice","Contrast","replicates")+
labs(y="Contrast",x="µm (relative to center)")
DCT_Shannon_entropy_plot <- data_to_plot %>%
filter(measured == "DCT-Shannon-entropy") %>%
line_plot(.,"slice_norm_micro","value","slice","DCT Shannon entropy","replicates")+
labs(y="Entropy",x="µm (relative to center)")
FRC_plot <- data_to_plot %>%
filter(measured == "FRC") %>%
line_plot(.,"slice_norm_micro","value","slice","FRC score (raw)","replicates") +
labs(y="FRC",x="µm (relative to center)")
#put all plots together
patch <- contrast_plot / DCT_Shannon_entropy_plot / FRC_plot
#plot
plot <- patch &
theme(panel.border = element_rect(size=1.5),
axis.text = element_text(face="bold",size=12),
strip.text = element_text(face="bold",size=14)
) &
scale_x_continuous(limits= c(min(data_to_plot$slice_norm_micro),max(data_to_plot$slice_norm_micro)),
breaks = c(-300,-150,0,150,300)) &
scale_color_manual(values = wes_palette("Moonrise3")) &
geom_vline(xintercept=selected_slices_micron,linetype='dashed',size=0.8,color="red")
ggsave(plot=plot, file="Figure_4_lineplots.png", type = "cairo-png", bg = "transparent",dpi = 300,height=8,width=16)
plot
Box Plot for Figure 4
#make sampling reproducible
set.seed(2020)
#take data_to_plot dataframe from previous chunk
#and select 200 slices per experiment (x 3 replicates x 3 protocols = 1800 slices in total)
selected_slices <- data_to_plot %>%
#only fused angles
filter(angle == "both_angles") %>%
#only both illuminations
filter(illu == "both_illuminations") %>%
#only draq5 staining
filter(staining == "draq5") %>%
mutate(name_slice = paste0(name,"_",slice)) %>%
group_by(orgID,protocol) %>%
sample_n(200) %>%
pull(name_slice)
#filter out FRC for only those slices
data_to_plot2_FRC <- data_to_plot %>%
mutate(name_slice = paste0(name,"_",slice)) %>%
filter(measured == "FRC") %>%
filter(name_slice %in% selected_slices)
boxplot_FRC <- ggplot(data_to_plot2_FRC, aes(y=value,x=protocol,group=protocol,fill=protocol))+
geom_violin(fill="lightgrey",alpha=0.8,size=0,inherit_aes=FALSE,outlier.shape=NA)+
geom_boxplot(width=0.25,size=1,alpha=.3,outlier.alpha = 1) +
scale_y_continuous(limits=c(0,max(data_to_plot2_FRC$value + 20)))+
geom_signif(comparisons = list(c("Fructose", "ScaleA2"),c("ClearT2","Fructose")),
map_signif_level=TRUE,y_position = c(52, 57),test = "wilcox.test",size=1)+
geom_signif(comparisons = list(c("ClearT2","ScaleA2")),
map_signif_level = TRUE,y_position=c(62),test = "wilcox.test",size=1)+
labs(x="protocol", y="FRC") +
scale_colour_discrete(name = "Organoid ID") +
theme_bw() +
theme(legend.position = "right")+
scale_fill_brewer(palette = "Set2")+
coord_flip()
#filter out DCT Shannon entropy values for the same slices
#so that we look at the same images in both plots
data_to_plot2_DCT_Shannon_entropy <- data_to_plot %>%
mutate(name_slice = paste0(name,"_",slice)) %>%
filter(measured == "DCT-Shannon-entropy") %>%
filter(name_slice %in% selected_slices)
boxplot_DCT_Shannon_Entropy <- ggplot(data_to_plot2_DCT_Shannon_entropy, aes(y=value,x=protocol,group=protocol,fill=protocol))+
geom_violin(fill="lightgrey",alpha=0.8,size=0,inherit_aes=FALSE,outlier.shape=NA)+
geom_boxplot(width=0.25,size=1,alpha=.3,outlier.alpha = 1) +
scale_y_continuous(limits=c(0,max(data_to_plot2_DCT_Shannon_entropy$value + 0.007)))+
geom_signif(comparisons = list(c("Fructose", "ScaleA2"),c("ClearT2","Fructose")),
map_signif_level=TRUE,y_position = c(0.015, 0.02),test = "wilcox.test",size=1)+
geom_signif(comparisons = list(c("ClearT2","ScaleA2")),
map_signif_level = TRUE,y_position=c(0.023),test = "wilcox.test",size=1)+
labs(x="protocol", y="Entropy") +
scale_colour_discrete(name = "Organoid ID") +
theme_bw() +
theme(legend.position = "right")+
scale_fill_brewer(palette = "Set2")+
coord_flip()
plot <- boxplot_FRC & theme(panel.border = element_rect(size=1.5),
axis.text = element_text(face="bold",size=12),
legend.direction = "vertical",
legend.key.height =unit(2, "cm"),
legend.key.width = unit(1,"cm"))
ggsave(plot=plot, file="Figure_4_FRC_boxplot.png", type = "cairo-png", bg = "transparent",dpi = 300,height=3.5,width=7)
plot
plot <- boxplot_DCT_Shannon_Entropy & theme(panel.border = element_rect(size=1.5),
axis.text = element_text(face="bold",size=12),
legend.direction = "vertical",
legend.key.height =unit(2, "cm"),
legend.key.width = unit(1,"cm"))
ggsave(plot=plot, file="Figure_4_DCT-Shannon-Entropy_boxplot.png", type = "cairo-png", bg = "transparent",dpi = 300,height=3.5,width=7)
plot
